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Recent studies have shown that due to the hammer effect of the governor, hydropower units are easily creating negative damping torque at the common mode frequency (below 0.1 Hz). Therefore, there is a risk of ultra low frequency oscillations (ULFO) in hydropower-dominated systems. ULFO is a small-signal frequency oscillation problem, which is quite different from low frequency oscillations (LFO). A conventional power system stabilizer (CPSS) has less effect on suppressing ULFO. To solve this problem, this paper proposes a high-order polynomial structure to replace the CPSS, and combine it with a proportional resonance controller to form a novel PR-PSS. In order to ensure the robustness of PR-PSS, based on the characteristic analysis results of the PR-PSS, a deep reinforcement learning (DRL) algorithm asynchronous advantage actor-critic (A3C) is introduced to train an agent. After training, the proposed agent can provide optimal parameter settings for PR-PSS under various operating conditions. Simulation results verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Due to the geographic features and fruitful water resources, the distributed generation of Small Hydropower Generator Stack (SHGS) are vigorously developed in southwest China. As the more concerns about security and stability of power grids with the large amount of SHGS feed into the grid, the accurate models are critical to analyze those potential impacts. With the development of online monitoring technologies in smart grid, PMU based dynamic equivalent modeling for SHGS drawn many attentions. However, field applications reveal that traditional SHGS equivalent model may not be robustness enough to adapt the different disturbances. To overcome this deficiency, method to improve the robustness of SHGS dynamic equivalent modeling is proposed. In the paper, a coherency identification based multi-machine modeling method is introduced to enhance the accuracy of equivalent model. Sensitivity and correlation analysis based key parameters selection scheme is implemented to select the critical parameters, meanwhile avoiding the multiple solutions problem. Moreover, multi-objective optimization algorithm is designed for key parameters identification of the equivalent model. Additionally, hybrid dynamic simulation has adopted into the equivalent process to improve the computational efficiency. The effectiveness of the proposed method has been validated with an actual case from China Southwest grids. [ABSTRACT FROM AUTHOR]